The document summarizes a research paper that proposes the VisualRank approach for image retrieval from large-scale image databases. It describes extracting visual features like texture, color, and gray histograms from images. Images are ranked based on measuring similarity between these extracted features. K-means clustering is used to group similar images, and minimum distance is calculated to retrieve images with maximum similarity to the query image. The implementation and results of applying VisualRank to an image database are discussed, showing it can effectively retrieve relevant images based on visual feature matching.

1. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology
Volume 1, Issue 4, June 2012
VisualRank for Image Retrieval from
Large-Scale Image Database
Suryakant P. Bhonge, Dr. D. S. Chaudhari, P. L. Paikrao

Abstract— VisualRank provide ranking among images to be significant implementation to the VisualRank for image
retrieved by measuring common visual features of the images. retrieval[1].
The similarity between images is measured by measuring There are two main challenges in captivating the concept
similarity within extracted features like Texture, Color and
Gray Histogram. Image ranked higher, when most of image
of inferring common visual themes to creating a scalable and
features matched to features of query image. In this paper, effective algorithm. The first challenge involved image
VisualRank approach is based on k-means clustering and processing required and seconds the need of evolving the
minimum distance findings among images is used. The results of mechanism for ranking images based on their similarity
experimental study of proposed algorithm are shown with matches.[5]
analysis of resultant image features. The images are retrieved The transformations of raw pixel data to a small set of
based on selection of images with maximum similarity features.
image regions were provided to image retrieval by applying
Index Terms— VisualRank, GLCM, K-means clustering segmentation. Regions are coherent in colors and texture.
These region properties were used for image retrieval[2]. The
descriptor and detector were developed for faster
I. INTRODUCTION computations and comparisons. It was found that the
correspondence between two images with respective
A huge amount of image data has been produced in
repeatability, distinctiveness and robustness was helpful.
diversified areas due to modernisation in engineering
Here corners, blob and T-junction of images were considered
practices. It becomes difficult and imperative problem in
or selected as point of interest, then feature vector was
searching images from varying collection of image
created having representation of neighbourhood of every
features[2]. Though image search is one of the most popular
interest point. Lastly minimum distances were found by
applications over internet but in most of search engines it
measuring Euclidian distance and depending on minimum
depends on text based searching method. Image retrieval
distance matching between different images were carried
process does not have active participation of image features.
out[6]. In Topic Sensitive PageRank approach, set of
Image feature extraction and image analysis is quite
PageRank vector was calculated offline for different topics,
complicated, time consuming and expensive process[1].
to produce a set of important score for a page with respect to
When a number of keywords added to the same database,
certain topics, rather than computing a rank vector for all web
there will be repeatedly problems due to differences in
pages[7].
sympathetic, reliability of awareness over time, etc[3], due to
W. Zhou et al. provide canonical image selection by
which image searching based on text search possesses some
selecting subset of photos, which represents most important
problems like relevancy.
and distinctive visual word of photo collection by using latent
When query with varying qualities like shape, size, color
visual context learning[8]. In canonical image selection,
etc is fired, less relevant or less important images may appear
images were selected in greedy fashions and used visual word
on the top and important or relevant images at the bottom of
of images and Affinity propagation [10] clustering for
the search result page[4].The reasons behind is difficulty in
similarity findings.
keywords association with images, large variable image
VisualRank approach depends on visual features among
qualities and semantic perception of images. VisualRank
the images that uses K-means clustering algorithm. In the
approach will significantly improve the image ranking when
implementation the images were retrieved using traditional
many of the images will contain same futures. In some of the
image retrieval method, after that the features like energy,
images these feature may occupy main portion of the image,
homogeneity, correlation, contrast, color and gray histogram
whereas in others, it may occupies only a small portion.
were extracted. Results were obtained by using K-means
Repetition of similarity futures among the images provides
clustering and then measurement of minimum distances
among the images. VisualRank to large-scale image search
Manuscript received May 28, 2012. using page ranking provides effective results of image
Suryakant P. Bhonge, Department of Electronics and retrieval.
Telecommunication Engineering., Government College of Engineering,
Amravati., (e-mail: suryakant.bhonge@gamil.com). India. In this paper image retrieval methods and actual
Dr. D. S. Chaudhari, Department of Electronics and Telecommunication implementation of VisualRank for Image retrieval is covered.
Engineering., Government College of Engineering, Amravati., India . This is followed by experimental results and discussions
P. L. Paikrao, Department of Electronics and Telecommunication
Engineering., Government College of Engineering, Amravati., India. worth.
51
All Rights Reserved © 2012 IJARCET

2. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology
Volume 1, Issue 4, June 2012
II. IMPLEMENTATION OF VISUALRANK
𝑃(𝑖,𝑗 )
To ensure the usefulness of VisualRank algorithm for 𝐻𝑜𝑚𝑜𝑔𝑒𝑛𝑒𝑖𝑡𝑦 = 𝑖,𝑗 1+|𝑖−𝑗 | (5)
image retrieval in real sense, experiments were conducted
using MatLab 7.10 environment on the images collected Color features contain values of R, G and B. For better
directly through Google Image. It was concentrated on the results rather taking color feature matching test for complete
200 small size image database with seven different query image, divided it into eight subregions.
images like “Taj Mahal”, “Coca Cola”, “Cap”, “Sea”, “Bat”,
“Bricks” and “Sprite”. In these four images from collection
of database images were retrieved based on their Texture,
Color and Gray Histogram features stored in xls file.
A. Feature Generation and Representation
The texture features were measured using Gray-Level
Co-occurrence Matrix (GLCM), It considered the spatial
relationship of pixels. The number of occurrence of pixel
pairs with certain values and specified spatial relationship
occurred in an image provides characteristics of texture
values by creating GLCM [9].
Normalized probability density Pδ(i,j) of the co-occurrence Fig. 1 Color Feature Extraction from Small Regions of
matrices can be defined as follows. Image
# 𝑝,𝑞 , 𝑝+𝑟,𝑞+𝑟 Є 𝐺 𝑓 𝑝,𝑞 =𝑖,𝑓 𝑝+𝑟,𝑞+𝑟 =𝑗 |} So that color feature contain in 8 × 3 matrix, measured
P 𝛿 (𝑖, 𝑗) = (1)
#𝐺 values of R, G, B for 8 subregions as shown in Fig. 1.
A histogram is a graphical representation showing a visual
Where, p, q = 0,1,…..M-1 are co-ordinates of the pixel, i,
impression of the distribution of data. For gray histogram
j = 0,1,…..L-1 are the gray levels, G is set of pixel pairs with
uses a default value of 256 bins and for binary image
certain relationship in the image. The number of elements in
histogram uses 2 bins.
G is obtained as #G. r is the distance between two pixels i and
j. Pδ(i,j) is the probability density that the first pixel has
B. Effecting Clustering
intensity value i and the second j, which separated by distance
δ=(rp, rq).[9]
K-means is one of the simplest learning algorithms that
Energy measures textural uniformity i.e. pixel pairs
solve the well known clustering problem. The main idea is to
repetitions. Energy is ranging 0 to 1 being 1 for a constant
define k centroids for k clusters, one for each cluster. The
image. It returns the sum of squared elements in the GLCM.
better choice is to place them as much as possible far away
Energy is given by
from each other. Here we initially made two centroids.
𝐸𝑛𝑒𝑟𝑔𝑦 = 𝑖,𝑗 𝑃(𝑖,𝑗 ) 2 (2)
Contrast is the difference in luminance and color that
makes an object distinguishable. It measures the local
variations in the Gray-Level Co-occurrence Matrix. Contrast
is 0 for a constant image and it is given by
Contrast= 𝑖,𝑗 |𝑖 − 𝑗|2 𝑃(𝑖,𝑗 ) (3)
A correlation function is the correlation between random
variable at two different points in space or time, usually as a
function of the spatial or temporal distance between the
points.
𝑖,𝑗 𝑖−𝜇𝑖 𝑗 −𝜇𝑗 𝑃(𝑖−𝑗 )
Correlation= (4) Fig. 2 Flowchart for K-means Clustering
𝜎𝑖 𝜎𝑗
Fig. 2 shows K-means clustering flowchart. Where, k is the
Where μi, μj, σi, σj are the means and standard deviations of number of clusters and x is the number of centroids. For
Pi and Pj respectively. Pi is the sum of each row in finding centroids select number of images from database. To
co-occurrence matrix and Pj is the sum of each column in the create grouping based on minimum distance such that each
co-occurrence matrix. group contain minimum q images and maximum p images
Homogeneity returns a value that measures the closeness measure distance between images and centroids. Image
of the distribution of elements in the GLCM to the GLCM database of 200 images were selected 14 maximum images
diagonal. It has Range from 0 to 1 and homogeneity is 1 for a and 4 minimum images for one cluster. When query was fired
diagonal GLCM. Homogeneity is given by
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All Rights Reserved © 2012 IJARCET

3. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology
Volume 1, Issue 4, June 2012
then based on query and cluster features, query finds the image and retrieval images as shown in Fig.4 (e). Gray
group of similar images having minimum image distance. histogram has values from 0 to 255, but starting values
The retrieval results are returned based on minimum distance provide good characteristic for matching features among
between the images inside cluster with query image. images. The gray histogram is shown in Fig. 4 (f), but only
starting 140 values were used with 48 color feature values
III. RESULTS AND DISCUSSIONS and 64 texture feature values in image retrieval.
In feature extraction, the color features were measured by 1
dividing original images into 16 subregions and color feature 0.8 query cap
Homogeneity
contains R, G and B components. Due to which each 0.6 Cap1
subregion having 1×3 values of color feature, so 16 0.4 Cap2
subregions are containing 16×3 values. Total 48 values for 0.2 Cap3
entire image are measured.
0 Cap4
The Gray Level Co-occurrence Matrix (GLCM) was
R1 R4 R7 R10 R13 R16
computed in four directions for 00, 450, 900, 1350. Based on regions
the GLCM four statistical parameters energy, contrast,
a) Homogeneity values
correlation and homogeneity were computed in four
directions at four points, so total 64 values of texture features 0.5
are returned. 0.4 Cap1
energy
0.3
Gray histogram representation having values 0 to 255, Cap2
0.2
which represent total representation of an image. For feature Cap3
0.1
matching process 1 to 140 values were used, which provide 0 Cap4
good similarity matching and they were stored, so total d1 (0,1) d1 (0,4) d1 d1 d1 query Cap
d1
extracted feature values of 252 for each image was presented. (45,3) (90,0) (135,-1) (135,-4)
directions
After completing feature extraction and storage of
database images, query image was fired and same six features
b) Energy values
of query image were measured. Fig. 3 shows the image
retrieval results for different query images. VisualRank
3
search for first four images retrieval from 200 database
Contrast
2 query Cap
1
images of different categories were shown that are relevant to 0 Cap1
the image query.
d3(135,-1)
d3(135,-4)
d1(0,4)
d3(90,0)2
d2(45,3)
d1 (0,1)
Cap2
Cap3
Cap4
pixels with direction
c) Contrast values
1
0.8 query Cap
correlation
0.6 Cap1
0.4
Cap2
0.2
0 Cap3
d1 (0,1) d1(0,4) d2(45,3) d3(90,0) d3(135,- d3(135,-
Cap4
1) 4)
pixels with directions
Fig. 3 Image Retrieval Results for different Query Images d) Correlation values
The retrieval results for “Cap” are shown in Fig. 3. The 160 Query R
120
extracted features values like homogeneity, energy, contrast, Query G
Color
80
correlation, colors were provided in Fig. 4. Texture feature 40 Query B
like energy, contrast, correlation, homogeneity were 0 Cap1 R
measured in four directions at four point of an image. The R1 R4 R7 R10 R13 R16
Cap1 G
direction 00, 450, 900 and 1350 are specified by offset value (0, Regions Cap1 B
1), (-1, 1), (-1, 0) and (-1, -1) respectively.
In texture feature homogeneity and correlation provide
good matching values than energy and contrast values as e) Color values
shown in Fig. 4. The energy, contrast, correlation and
homogeneity were having total 64 values, but single color
feature was containing total 48 values. Comparing to texture
features, color feature were highly matched among query
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All Rights Reserved © 2012 IJARCET

4. ISSN: 2278 – 1323
International Journal of Advanced Research in Computer Engineering & Technology
Volume 1, Issue 4, June 2012
[9] Dr. H. B. Kekre, S. D. Thepade, T. K. Sarode and V. Suryawanshi,
„Image Retrieval using Texture Features extracted from GLCM, LBG
2000
and KPE‟, International Journal of Computer Theory and
query Cap
Engineering, 2(5), October, 2010.
Gray Hostogram
1500
1000 [10] W. Triggs, “Detecting keypoints with stable position, orientation and
500 Cap1
0 scale under illumination changes,” in Proceedings of the European
Cap2
Conference on Computer Vision, vol. 4, pp. 100–113, 2004.
101
151
201
251
1
51
Cap3
Pixels Cap4
f) Gray Histogram values Suryakant P. Bhonge received the B.E. degree in
Fig. 4 Extracted features values for retrieval images of Electronics and telecommunication engineering from
query “Cap” the Sant Gadge Baba, Amravati University in 2008,
and he is currently pursuing the M. Tech. degree in
Electronic System and Communication (ESC) at
So combination of all values total 252 features values of Government College of Engineering Amravati. He
energy, contrast, correlation, homogeneity, color and gray has attended one day workshops on “VLSI & EDA
Tools & Technology in Education‟ and
histogram were used to find similarity among the images. But “Cadence-OrCad EDA Technology‟ at Government
color features were dominant in image retrieval results than College of Engineering Amravati. He also participated in “National Level
texture and gray histogram features. Technical Festival – PERSUIT 2K8” at SSGMC, Shegaon and
“TECHNOCELLENCE-2008” at SSGBCOE, Bhusawal. Also he was
VisualRank provide relevant images from database worked as a coordinator in National Level Technical Festival- PRANETA
depending on the similarities among the images. The feature 2008 at J.D.I.E.T., Yavtmal. He is a member of the ISTE.
extraction of database images was take some time, but once
Devendra S. Chaudhari obtained BE, ME, from
it completed then there was no need to follow feature Marathwada University, Aurangabad and PhD from
extraction process again. The image retrieval results were Indian Institute of Technology Bombay, Powai,
return depending on random weightage of highest similarity Mumbai. He has been engaged in teaching, research for
matched images. period of about 25 years and worked on DST-SERC
sponsored Fast Track Project for Young Scientists. He
has worked as Head Electronics and
Telecommunication, Instrumentation, Electrical,
IV. CONCLUSIONS Research and incharge Principal at Government
Engineering Colleges. Presently he is working as Head, Department of
The VisualRank provide simple mechanism for image Electronics and Telecommunication Engineering at Government College of
retrieval by taking in to account minimum distances among Engineering, Amravati. Dr. Chaudhari published research papers and
presented papers in international conferences abroad at Seattle, USA and
the images. After using VisualRank, the relevant images Austria, Europe. He worked as Chairman / Expert Member on different
were returned at the top and if irrelevant images present are committees of All India Council for Technical Education, Directorate of
returned at the bottom in image search results. The similarity Technical Education for Approval, Graduation, Inspection, Variation of
Intake of diploma and degree Engineering Institutions. As a university
measurement of images was based on the common visual recognized PhD research supervisor in Electronics and Computer Science
feature between the images. The images having more Engineering he has been supervising research work since 2001. One research
weightage than other images were ranked higher in image scholar received PhD under his supervision.
He has worked as Chairman / Member on different university and college
retrieval. Image clustering and finding the minimum distance level committees like Examination, Academic, Senate, Board of Studies, etc.
among the images provides image retrieval results. he chaired one of the Technical sessions of International Conference held at
VisualRank provide additional feature to current image Nagpur. He is fellow of IE, IETE and life member of ISTE, BMESI and
member of IEEE (2007). He is recipient of Best Engineering College
search methods for efficient performance.
Teacher Award of ISTE, New Delhi, Gold Medal Award of IETE, New
Delhi, Engineering Achievement Award of IE (I), Nashik. He has organized
REFERENCES various Continuing Education Programmes and delivered Expert Lectures on
research at different places. He has also worked as ISTE Visiting Professor
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Image Search”, IEEE Transactions on Pattern Analysis And Machine Thailand. His present research and teaching interests are in the field of
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Image Segmentation Using Expectation-Maximization and Its
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Machine Intelligence, vol. 24, no. 8, pp. 1026-1038, Aug. 2002. Industrial Electronics from Dr. BAM University,
[3] M. Ferecatu, “Image retrieval with active relevance feedback using Aurangabad in 2003 and the M. Tech. degree in
both visual and keyword-based descriptors”, Ph. D. Thesis, University Electronics from SGGSIE&T, Nanded in 2006. He
of Versailles Saint-Quentin-En-Yvelines, France. is working as Assistant Professor, Electronics and
[4] B. V. Keong, P. Anthony, “PageRank: A Modified Random Surfer Telecommunication Engineering Department,
Model”, 7th International Conference on IT in Asia (CITA), 2011. Government College of Engineering Amravati. He
[5] Y. Jing, S. Baluja, “PageRank for Product Image Search”, has attended An International Workshop on Global
International World Wide Web Conference Committee (IW3C2). 2008, ICT Standardization Forum for India (AICTE Delhi
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Features,” Proc. Ninth European Conf. Computer Vision, pp. Macfarlane, Glasgow, Scotland. He has recently published the papers in
404-417,2006. conference on „Filtering Audio Signal by using Blackfin BF533EZ kit lite
[7] T. Haveliwala, “Topic-Sensitive Pagerank: A Context-Sensitive evaluation board and visual DSP++‟ and „Project Aura: Towards
Ranking Algorithm for Web Search,” IEEE Trans. Knowledge and Acquiescent Pervasive Computing‟ in National Level Technical
Data Eng., vol. 15, no. 4, pp. 784-796, July/Aug. 2003. Colloquium “Technozest-2K11”, at AVCOE, Sangamner on February 23rd,
[8] W. Zhou, Y. Lu. H. Li and Q. Tian. “Canonical Image Selection by 2011. He is a member of the ISTE and the IETE.
Visual Context Learning” International Conference on Pattern
Recognition 2010.
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